text: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import _init_paths
  import os
  import sys
  import numpy as np
  import argparse
  import pprint
  import pdb
  import time
  import cv2
  
  import torch
  from torch.autograd import Variable
  import torch.nn as nn
  import torch.nn.functional as F
  import torch.optim as optim
  import pickle
  from roi_data_layer.roidb import combined_roidb
  from roi_data_layer.roibatchLoader import roibatchLoader
  from model.utils.config import cfg, cfg_from_file, cfg_from_list, get_output_dir
  from model.rpn.bbox_transform import clip_boxes
  # from model.nms.nms_wrapper import nms
  from model.roi_layers import nms
  from model.rpn.bbox_transform import bbox_transform_inv
  from model.utils.net_utils import save_net, load_net, vis_detections, vis_detections_filtered_objects_PIL
  from model.faster_rcnn.vgg16 import vgg16
  from model.faster_rcnn.resnet import resnet
  
  try:
      xrange  # Python 2
  except NameError:
      xrange = range  # Python 3
  
  
  def parse_args():
      """
      Parse input arguments
      """
      parser = argparse.ArgumentParser(description='Train a Fast R-CNN network')
      parser.add_argument('--dataset', dest='dataset',
                          help='training dataset',
                          default='pascal_voc', type=str)
      parser.add_argument('--cfg', dest='cfg_file',
                          help='optional config file',
                          default='cfgs/resnet101.yml', type=str)
      parser.add_argument('--net', dest='net',
                          help='vgg16, res50, res101, res152',
                          default='res101', type=str)
      parser.add_argument('--set', dest='set_cfgs',
                          help='set config keys', default=None,
                          nargs=argparse.REMAINDER)
      parser.add_argument('--load_dir', dest='load_dir',
                          help='directory to load models',
                          default="models", type=str)
      parser.add_argument('--cuda', dest='cuda',
                          help='whether use CUDA',
                          action='store_true')
      parser.add_argument('--ls', dest='large_scale',
                          help='whether use large imag scale',
                          action='store_true')
      parser.add_argument('--mGPUs', dest='mGPUs',
                          help='whether use multiple GPUs',
                          action='store_true')
      parser.add_argument('--cag', dest='class_agnostic',
                          help='whether perform class_agnostic bbox regression',
                          action='store_true')
      parser.add_argument('--parallel_type', dest='parallel_type',
                          help='which part of model to parallel, 0: all, 1: model before roi pooling',
                          default=0, type=int)
      parser.add_argument('--checksession', dest='checksession',
                          help='checksession to load model',
                          default=1, type=int)
      parser.add_argument('--checkepoch', dest='checkepoch',
                          help='checkepoch to load network',
                          default=8, type=int)
      parser.add_argument('--checkpoint', dest='checkpoint',
                          help='checkpoint to load network',
                          default=89999, type=int)
      parser.add_argument('--vis', dest='vis',
                          help='visualization mode',
                          action='store_true')
  
      parser.add_argument('--model_name',
                          help='directory to load models', default='handobj_100K',
                          required=False, type=str)
      parser.add_argument('--save_name',
                          help='folder to save eval results',
                          required=True)
      parser.add_argument('--thresh_hand',
                          type=float, default=0.1,
                          required=False)
      parser.add_argument('--thresh_obj', default=0.1,
                          type=float,
                          required=False)
  
      args = parser.parse_args()
      return args
  
  
  lr = cfg.TRAIN.LEARNING_RATE
  momentum = cfg.TRAIN.MOMENTUM
  weight_decay = cfg.TRAIN.WEIGHT_DECAY
  
  
  if __name__ == '__main__':
      args = parse_args()
      print('Called with args:')
      print(args)
  
      if torch.cuda.is_available() and not args.cuda:
          print("WARNING: You have a CUDA device, so you should probably run with --cuda")
  
      np.random.seed(cfg.RNG_SEED)
      if args.dataset == "pascal_voc":
          args.imdb_name = "voc_2007_trainval"
          args.imdbval_name = "voc_2007_test"
          args.set_cfgs = ['ANCHOR_SCALES', '[8, 16, 32, 64]', 'ANCHOR_RATIOS', '[0.5,1,2]']
  
      args.cfg_file = "cfgs/{}_ls.yml".format(args.net) if args.large_scale else "cfgs/{}.yml".format(args.net)
  
      if args.cfg_file is not None:
          cfg_from_file(args.cfg_file)
      if args.set_cfgs is not None:
          cfg_from_list(args.set_cfgs)
  
      print('Using config:')
      pprint.pprint(cfg)
  
      cfg.TRAIN.USE_FLIPPED = False
      imdb, roidb, ratio_list, ratio_index = combined_roidb(args.imdbval_name, False)
      imdb.competition_mode(on=True)
  
      print('{:d} roidb entries'.format(len(roidb)))
  
      input_dir = args.load_dir + "/" + args.net + "_" + args.model_name + "/" + args.dataset
  
      if not os.path.exists(input_dir):
          raise Exception('There is no input directory for loading network from ' + input_dir)
      load_name = os.path.join(input_dir,
                               'faster_rcnn_{}_{}_{}.pth'.format(args.checksession, args.checkepoch, args.checkpoint))
      print(f'\n ---------> which model = {load_name}\n')
  
      # initialize the network here.
      if args.net == 'vgg16':
          fasterRCNN = vgg16(imdb.classes, pretrained=False, class_agnostic=args.class_agnostic)
      elif args.net == 'res101':
          fasterRCNN = resnet(imdb.classes, 101, pretrained=False, class_agnostic=args.class_agnostic)
      elif args.net == 'res50':
          fasterRCNN = resnet(imdb.classes, 50, pretrained=False, class_agnostic=args.class_agnostic)
      elif args.net == 'res152':
          fasterRCNN = resnet(imdb.classes, 152, pretrained=False, class_agnostic=args.class_agnostic)
      else:
          print("network is not defined")
          pdb.set_trace()
  
      fasterRCNN.create_architecture()
  
      print("load checkpoint %s" % (load_name))
      checkpoint = torch.load(load_name)
      fasterRCNN.load_state_dict(checkpoint['model'])
      if 'pooling_mode' in checkpoint.keys():
          cfg.POOLING_MODE = checkpoint['pooling_mode']
  
      print('load model successfully!')
      # initialize the tensor holder here.
      im_data = torch.FloatTensor(1)
      im_info = torch.FloatTensor(1)
      num_boxes = torch.LongTensor(1)
      gt_boxes = torch.FloatTensor(1)
      box_info = torch.FloatTensor(1)
  
      pascal_classes = np.asarray(['__background__', 'targetobject', 'hand'])
  
      # ship to cuda
      if args.cuda:
          im_data = im_data.cuda()
          im_info = im_info.cuda()
          num_boxes = num_boxes.cuda()
          gt_boxes = gt_boxes.cuda()
  
      # make variable
      im_data = Variable(im_data)
      im_info = Variable(im_info)
      num_boxes = Variable(num_boxes)
      gt_boxes = Variable(gt_boxes)
  
      if args.cuda:
          cfg.CUDA = True
  
      if args.cuda:
          fasterRCNN.cuda()
  
      start = time.time()
      max_per_image = 100
  
      vis = args.vis
  
      print(f'\n---------> det score thres_hand = {args.thresh_hand}\n')
      print(f'\n---------> det score thres_obj = {args.thresh_obj}\n')
  
      save_name = args.save_name
      num_images = len(imdb.image_index)
      all_boxes = [[[] for _ in xrange(num_images)]
                   for _ in xrange(imdb.num_classes)]
  
      output_dir = get_output_dir(imdb, save_name)
      dataset = roibatchLoader(roidb, ratio_list, ratio_index, 1, imdb.num_classes, training=False, normalize=False)
      dataloader = torch.utils.data.DataLoader(dataset, batch_size=1,
                                               shuffle=False, num_workers=0,
                                               pin_memory=True)
  
      data_iter = iter(dataloader)
  
      _t = {'im_detect': time.time(), 'misc': time.time()}
      det_file = os.path.join(output_dir, 'detections.pkl')
  
      fasterRCNN.eval()
      empty_array = np.transpose(np.array([[], [], [], [], []]), (1, 0))
      for i in range(num_images):
  
          data = next(data_iter)
          with torch.no_grad():
              im_data.resize_(data[0].size()).copy_(data[0])
              im_info.resize_(data[1].size()).copy_(data[1])
              gt_boxes.resize_(data[2].size()).copy_(data[2])
              num_boxes.resize_(data[3].size()).copy_(data[3])
              box_info.resize_(data[4].size()).copy_(data[4])
  
          det_tic = time.time()
          rois, cls_prob, bbox_pred, \
          rpn_loss_cls, rpn_loss_box, \
          RCNN_loss_cls, RCNN_loss_bbox, \
          rois_label, loss_list = fasterRCNN(im_data, im_info, gt_boxes, num_boxes, box_info)
  
          scores = cls_prob.data
          boxes = rois.data[:, :, 1:5]
          hand_contacts = loss_list[0][0]
          hand_vector = loss_list[1][0].detach()
          lr_vector = loss_list[2][0].detach()
  
          ##### hand contact ########
          maxs, indices = torch.max(hand_contacts, 2)
          indices = indices.squeeze(0).unsqueeze(-1).float()
          nc_prob = F.softmax(hand_contacts[:, :, 0].squeeze(0).unsqueeze(-1).float().detach())
          # print(hand_contacts.shape)
          ###########################
          lr = F.sigmoid(lr_vector) > 0.5
          lr = lr.squeeze(0).float()
  
          if cfg.TEST.BBOX_REG:
              # Apply bounding-box regression deltas
              box_deltas = bbox_pred.data
              if cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED:
                  # Optionally normalize targets by a precomputed mean and stdev
                  if args.class_agnostic:
                      box_deltas = box_deltas.view(-1, 4) * torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_STDS).cuda() \
                                   + torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_MEANS).cuda()
                      box_deltas = box_deltas.view(1, -1, 4)
                  else:
                      box_deltas = box_deltas.view(-1, 4) * torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_STDS).cuda() \
                                   + torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_MEANS).cuda()
                      box_deltas = box_deltas.view(1, -1, 4 * len(imdb.classes))
  
              pred_boxes = bbox_transform_inv(boxes, box_deltas, 1)
              pred_boxes = clip_boxes(pred_boxes, im_info.data, 1)
          else:
              # Simply repeat the boxes, once for each class
              pred_boxes = np.tile(boxes, (1, scores.shape[1]))
  
          pred_boxes /= data[1][0][2].item()
  
          scores = scores.squeeze()
          pred_boxes = pred_boxes.squeeze()
          det_toc = time.time()
          detect_time = det_toc - det_tic
          misc_tic = time.time()
          if args.vis:
              im = cv2.imread(imdb.image_path_at(i))
              im2show = np.copy(im)
          for j in xrange(1, imdb.num_classes):
  
              # inds = torch.nonzero(scores[:,j]>thresh).view(-1)
              if pascal_classes[j] == 'hand':
                  inds = torch.nonzero(scores[:, j] > args.thresh_hand).view(-1)
              elif pascal_classes[j] == 'targetobject':
                  inds = torch.nonzero(scores[:, j] > args.thresh_obj).view(-1)
              else:
                  inds = torch.nonzero(scores[:, j] > args.thresh_obj).view(-1)
  
              # if there is det
              if inds.numel() > 0:
                  cls_scores = scores[:, j][inds]
                  _, order = torch.sort(cls_scores, 0, True)
                  if args.class_agnostic:
                      cls_boxes = pred_boxes[inds, :]
                  else:
                      cls_boxes = pred_boxes[inds][:, j * 4:(j + 1) * 4]
                  cls_dets = torch.cat((cls_boxes, cls_scores.unsqueeze(1), indices[inds, :],
                                        hand_vector.squeeze(0)[inds, :], lr[inds, :], nc_prob[inds, :]), 1)
                  cls_dets = cls_dets[order]
                  keep = nms(cls_boxes[order, :], cls_scores[order], cfg.TEST.NMS)
                  cls_dets = cls_dets[keep.view(-1).long()]
                  if args.vis:
                      im2show = vis_detections_filtered_objects_PIL(im2show, imdb.classes[j], cls_dets.cpu().numpy(), 0.1)
                  all_boxes[j][i] = cls_dets.cpu().numpy()
              else:
                  all_boxes[j][i] = empty_array
  
          # Limit to max_per_image detections *over all classes*
          if max_per_image > 0:
              image_scores = np.hstack([all_boxes[j][i][:, 4]
                                        for j in xrange(1, imdb.num_classes)])
              if len(image_scores) > max_per_image:
                  image_thresh = np.sort(image_scores)[-max_per_image]
                  for j in xrange(1, imdb.num_classes):
                      keep = np.where(all_boxes[j][i][:, 4] >= image_thresh)[0]
                      all_boxes[j][i] = all_boxes[j][i][keep, :]
  
          misc_toc = time.time()
          nms_time = misc_toc - misc_tic
  
          sys.stdout.write('im_detect: {:d}/{:d} {:.3f}s {:.3f}s   \r' \
                           .format(i + 1, num_images, detect_time, nms_time))
          sys.stdout.flush()
  
      with open(det_file, 'wb') as f:
          pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL)
  
      # entrance AP calculation  --> goes to pascal_voc.py
      print('Evaluating detections......................................')
      imdb.evaluate_detections(all_boxes, output_dir)
  
      end = time.time()
      print("test time: %0.4fs" % (end - start))
  